Optimizing Quantum Optimization

          @misc{ scivideos_PIRSA:19070008,
            doi = {10.48660/19070008},
            url = {https://pirsa.org/19070008},
            author = {Leichenauer, Stefan},
            keywords = {Quantum Matter},
            language = {en},
            title = {Optimizing Quantum Optimization},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {jul},
            note = {PIRSA:19070008 see, \url{https://scivideos.org/pirsa/19070008}}
          }
          

Abstract

Variational algorithms for a gate-based quantum computer, like the QAOA, prescribe a fixed circuit ansatz --- up to a set of continuous parameters --- that is designed to find a low-energy state of a given target Hamiltonian. After reviewing the relevant aspects of the QAOA, I will describe attempts to make the algorithm more efficient. The strategies I will explore are 1) tuning the variational objective function away from the energy expectation value, 2) analytical estimates that allow elimination of some of the gates in the QAOA circuit, and 3) using methods of machine learning to search the design space of nearby circuits for improvements to the original ansatz. While there is evidence of room for improvement in the circuit ansatz, finding an ML algorithm to effect that improvement remains an outstanding challenge.